Stacked Gaussian Process Learning. - Marion Neumann; Kristian Kersting; Zhao Xu 001; Daniel Schulz (ICDM, 2009)

Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation. - Marion Neumann; Babak Ahmadi; Kristian Kersting (AAAI, 2011)

Efficient Graph Kernels by Randomization. - Marion Neumann; Novi Patricia; Roman Garnett; Kristian Kersting (ECML/PKDD (1), 2012)

Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. - Martin Schiegg; Marion Neumann; Kristian Kersting (AISTATS, 2012)

Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels. - Marion Neumann; Roman Garnett; Kristian Kersting (ACML, 2013)

Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels. - Nils M. Kriege; Marion Neumann; Kristian Kersting; Petra Mutzel (ICDM, 2014)

Erosion Band Features for Cell Phone Image Based Plant Disease Classification. - Marion Neumann; Lisa Hallau; Benjamin Klatt; Kristian Kersting; Christian Bauckhage (ICPR, 2014)

High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline. - Laura Antanas; Plinio Moreno; Marion Neumann; Rui Pimentel de Figueiredo; Kristian Kersting; José Santos-Victor; Luc De Raedt (CoRR, 2014)

Propagation Kernels. - Marion Neumann; Roman Garnett; Christian Bauckhage; Kristian Kersting (CoRR, 2014)

pyGPs: a Python library for Gaussian process regression and classification. - Marion Neumann; Shan Huang; Daniel E. Marthaler; Kristian Kersting (J. Mach. Learn. Res., 2015)

Learning with Graphs using Kernels from Propagated Information. - Marion Neumann (2015)

Propagation kernels: efficient graph kernels from propagated information. - Marion Neumann; Roman Garnett; Christian Bauckhage; Kristian Kersting (Mach. Learn., 2016)

A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels. - Nils M. Kriege; Marion Neumann; Christopher Morris 001; Kristian Kersting; Petra Mutzel (CoRR, 2017)

An End-to-End Deep Learning Architecture for Graph Classification. - Muhan Zhang; Zhicheng Cui; Marion Neumann; Yixin Chen 001 (AAAI, 2018)

AI profiles: an interview with Kristian Kersting. - Marion Neumann (AI Matters, 2018)

AI profiles: an interview with Iolanda Leite. - Marion Neumann (AI Matters, 2018)

Model AI Assignments 2019. - Todd W. Neller; Raja Sooriamurthi; Michael Guerzhoy; Lisa Zhang; Paul G. Talaga; Christopher Archibald; Adam Summerville; Joseph C. Osborn; Cinjon Resnick; Avital Oliver; Surya Bhupatiraju; Kumar Krishna Agrawal; et al. (AAAI, 2019)

ACM SIGAI activity report. - Sven Koenig; Sanmay Das; Rosemary D. Paradis; John P. Dickerson; Yolanda Gil; Katherine Guo; Benjamin Kuipers; Iolanda Leite; Hang Ma 001; Nicholas Mattei; Amy McGovern; Larry R. Medsker; et al. (AI Matters, 2019)

AI profiles: an interview with Thomas Dietterich. - Marion Neumann (AI Matters, 2019)

AI profiles: an interview with Leslie Kaelbling. - Marion Neumann (AI Matters, 2019)

AI education matters: a first introduction to modeling and learning using the data science workflow. - Marion Neumann (AI Matters, 2019)

Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. - Laura Antanas; Plinio Moreno; Marion Neumann; Rui Pimentel de Figueiredo; Kristian Kersting; José Santos-Victor; Luc De Raedt (Auton. Robots, 2019)

A unifying view of explicit and implicit feature maps of graph kernels. - Nils M. Kriege; Marion Neumann; Christopher Morris 001; Kristian Kersting; Petra Mutzel (Data Min. Knowl. Discov., 2019)

Model AI Assignments 2020. - Todd W. Neller; Stephen Keeley; Michael Guerzhoy; Wolfgang Hönig; Jiaoyang Li 001; Sven Koenig; Ameet Soni; Krista Thomason; Lisa Zhang; Bibin Sebastian; Cinjon Resnick; Avital Oliver; et al. (AAAI, 2020)

TUDataset: A collection of benchmark datasets for learning with graphs. - Christopher Morris 001; Nils M. Kriege; Franka Bause; Kristian Kersting; Petra Mutzel; Marion Neumann (CoRR, 2020)

Capturing Student Feedback and Emotions in Large Computing Courses: A Sentiment Analysis Approach. - Marion Neumann; Robin Linzmayer (SIGCSE, 2021)

EAAI-22 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. - Michael Guerzhoy; Marion Neumann; Emmanuel Johnson; David Johnson 006; Henry Chai; Daniel Garijo; Zhuoyue Lyu; Christopher J. MacLellan (AI Matters, 2022)

EAAI-23 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. - Michael Guerzhoy; Marion Neumann; Pat Virtue; Carolyn Jane Anderson; Yaman Kumar Singla; Alexi Orchard; Rajiv Ratn Shah (AI Matters, 2023)

EAAI-24 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program. - Marion Neumann; Stephanie Rosenthal; Justin Stevens 001; Rachel Lugo; William Agnew; Shira Wein (AI Matters, 2024)